Global GRACE Data Assimilation for Groundwater and Drought Monitoring

Advances and Challenges

Journal article (2019)

Authors

Bailing Li NASA Goddard Space Flight Center, University of Maryland
Matthew Rodell NASA Goddard Space Flight Center
Sujay Kumar NASA Goddard Space Flight Center
Hiroko Kato Beaudoing University of Maryland, NASA Goddard Space Flight Center
Augusto Getirana University of Maryland, NASA Goddard Space Flight Center
Benjamin F. Zaitchik Johns Hopkins University
Luis Gustavo de Goncalves National Institute for Space Research
Camila Cossetin Climatempo
S.C. Steele-Dunne Water Resources - CEG
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Research Group
Water Resources (CEG) (TU Delft)
Copyright: © 2019 Bailing Li, Matthew Rodell, Sujay Kumar, Hiroko Kato Beaudoing, Augusto Getirana, Benjamin F. Zaitchik, Luis Gustavo de Goncalves, Camila Cossetin, S.C. Steele-Dunne, More Authors
DOI
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https://doi.org/10.1029/2018WR024618
Global GRACE data assimilation Global groundwater drought monitoring Groundwater drought Groundwater storage estimates Groundwater temporal variability
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Published Date

2019

Language

English

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Faculty

Civil Engineering & Geosciences

Department

Water Management

Research Group

Water Resources

Abstract

The scarcity of groundwater storage change data at the global scale hinders our ability to monitor groundwater resources effectively. In this study, we assimilate a state-of-the-art terrestrial water storage product derived from Gravity Recovery and Climate Experiment (GRACE) satellite observations into NASA's Catchment land surface model (CLSM) at the global scale, with the goal of generating groundwater storage time series that are useful for drought monitoring and other applications. Evaluation using in situ data from nearly 4,000 wells shows that GRACE data assimilation improves the simulation of groundwater, with estimation errors reduced by 36% and 10% and correlation improved by 16% and 22% at the regional and point scales, respectively. The biggest improvements are observed in regions with large interannual variability in precipitation, where simulated groundwater responds too strongly to changes in atmospheric forcing. The positive impacts of GRACE data assimilation are further demonstrated using observed low-flow data. CLSM and GRACE data assimilation performance is also examined across different permeability categories. The evaluation reveals that GRACE data assimilation fails to compensate for the lack of a groundwater withdrawal scheme in CLSM when it comes to simulating realistic groundwater variations in regions with intensive groundwater abstraction. CLSM-simulated groundwater correlates strongly with 12-month precipitation anomalies in low-latitude and midlatitude areas. A groundwater drought indicator based on GRACE data assimilation generally agrees with other regional-scale drought indicators, with discrepancies mainly in their estimated drought severity.